"2019 is the year of the data engineer. Data engineers will find themselves in high demand—they specialize in translating the work of data scientists into hardened, data-driven software solutions for the business."

As data sets have grown, rendering large amounts of data with traditional architectures has become harder, said Nima Negahban, CTO and co-founder at Kinetica. GPUs used with direct memory access can help crunch large volumes of data faster and more efficiently. This makes it easier to build high-definition visualizations on the server side that simply get served by the application via a web application.

"We’re excited to partner with NVIDIA in this journey to democratize AI — with NVIDIA driving model development and training and Kinetica driving operationalization and deployment of those models, enabling enterprises to gain maximum insight from their data.”

We live in a new era where everything that CMOs do is data-driven, says Daniel Raskin, CMO at San Francisco-based startup Kinetica. And the businesses that can most effectively leverage the use of data will come out ahead by offering a better customer experience.

Now that we’ve barreled past this era and into the Extreme Data Economy, businesses—and even whole economies—are becoming powered by data, so much so that the data generated from conducting business can become more valuable than the actual business itself.

Data is the glue across all channels, and companies will need to embrace business-differentiating data innovations, from artificial intelligence to using super-fast GPUs, that meet customers wherever they are.

There is more data coming back to and being generated by organizations than ever before and it will only get more complex. This means that organizations need to think about how to simplify their data architectures to grow and thrive in the extreme data economy

OVO’s OVO Analytics named as Digital Disruptor of the Year. As part of Lippo Group, OVO provides highly personalized offers and services through a big data analytics platform that integrates information from organizations under the Lippo Group. T

Kinetica is empowering automakers, suppliers, and associated start-ups to speed up how vehicle data insight is engendered by bringing together the accelerated analytics of a GPU database, real-time location intelligence, and the competency of artificial intelligence.

With this increase in data sources and complexity of analysis, the key question for operators is: how can you leverage this extreme data to retain customers, improve and expand your business operations?

An insight engine, with a GPU database at its core, combines Advanced Analytics, visual discovery, location intelligence, and Machine Learning within a single engine. All these capabilities will be needed for increasingly complex analysis and conducting IVA at scale.

Data insights company Kinetica expanded on its partnership with Dell EMC this week. The two have collaborated to offer a bundled solution to help clients quickly gather actionable insights from raw data.

The most lucrative use cases for the IoT require acting in real time on continuously generated streaming data from sources like industrial equipment sensors, autonomous or connected vehicles, or physical infrastructure for smart cities.

Unlike societies, economies, and businesses that existed in the prior Industrial Revolutions, however, we need to be proactive in how we mitigate and minimize the impact of these transformational changes on those people who would be disadvantaged by them.

I am passionate about aligning technology to solving the business problems that matter. To me, this is about turning real-time analytics from a science experiment into technology businesses can immediately put to use and understand.

We have moved beyond the big data era into the Extreme Data Economy. In this new world, businesses need to translate massive volumes of complex data at unparalleled speed into omnichannel insight, with streaming data analysis, visual foresight and streamlined machine learning.

As data from the Internet of Things (IoT) increased, businesses started dealing with the challenge of analyzing streaming data in real time. At present, GPUs offer the most cost-effective solution for large amounts of data being streamed in real time

Yesterday’s technologies won’t solve today’s problems. We seem to be learning this over and over again. While that is the bad news, the good news is that there are also brilliant minds that create new technologies to solve those problems.

To integrate data-driven AI into operationalized pipelines, organizations will need a single platform capable of streamlining, automating and managing the entire Machine Learning and Deep Learning lifecycles.